With little chance for discovery and decreasing budgets, yet sustained pressure to publish, the unethical practices of duplicate publications and plagiarism are significant. With no robust method to identify existing and potential duplicate scientific articles by editors and reviewers means that this can go unchecked, until now. eTBLAST, a text similarity search tool available to all on the web, has been used to demonstrate that we can detect with high sensitivity and specificity putative duplicate/plagiarized articles by systematically comparing each Medline abstract (or abstract in review) to all other Medline records. We hypothesize that rigorous identification of purveyors of this behavior, the exhaustive tagging of duplicate articles and the availability of a search tool customized for use by editors, reviewers, granting officials, etc. to detect potential problem manuscripts before they are accepted for publication will be a substantial deterrent, ultimately improving the quality of reported science for all. We will address this through the following specific aims: 1) Refine statistical predictors, thresholds, signatures and algorithms to maximize the efficiency by which we can detect putative duplicate and plagiarized articles within Medline. 2) Systematically check every Medline record against every other to develop a public database of questionable articles that have been reviewed/verified manually to assign a probability of duplication. 3) Perform an analysis of trends, rates and any statistically relevant distributions to understand and address root causes for this behavior. 4) Create a secure resource that is available and open to all journals/reviewers, thus enabling them to estimate novelty and probable overlap with previous publications prior to acceptance. [unreadable] [unreadable] [unreadable]